1. Field of the Invention
The present invention relates generally to a computerized method and system to aid radiologists in detection of abnormalities in tomography scans.
The present invention also generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; 5,732,697; 5,740,268; 5,790,690; 5,832,103; 5,873,824; 5,881,124; 5,931,780; 5,974,165; 5,982,915; 5,984,870; 5,987,345; and 6,011,862; as well as U.S. patent application Ser. Nos. 08/173,935; 08/398,307 (PCT Publication WO 96/27846); 08/536,149; 08/562,087; 08/900,188; 08/900,189; 08/900,191; 08/900,361; 08/979,623; 08/979,639; 08/982,282; 09/027,468; 09/027,685; 09/028,518; 09/053,798; 09/092,004; 09/121,719; 09/131,162; 09/141,535; 09/156,413; 09/298,852; and 09/471,088; PCT patent applications PCT/US99/24007; PCT/US99/25998; and U.S. provisional patent application No. 60/160,790 and Attorney Docket Number 0730-0069-20 PROV filed on Jan. 18, 2000, all of which are incorporated herein by reference. Of these patents and applications, U.S. Pat. Nos. 5,463,548; 5,622,171; 5,732,697; and 5,873,824; patent application Ser. Nos. 08/562,087; 08/900,361; and 09/027,685; and U.S. provisional patent application No. 60/160,790 and Attorney Docket Number 0730-0069-20 PROV filed on Jan. 18, 2000 are of particular interest.
The present invention includes use of various technologies referenced and described in the above-noted U.S. Patents and Applications, as well as described in the references identified in the appended LIST OF REFERENCES by the author(s) and year of publication and cross-referenced throughout the specification by numerals in brackets corresponding to the respective references, the entire contents of which, including the related patents and applications listed above and references listed in the LIST OF REFERENCES, are incorporated herein by reference.
2. Discussion of the Background
Lung cancer will result in an estimated 158,900 deaths in the United States in 1999 and ranks as the leading cause of cancer death in American men and women [1]. Lung cancer is responsible for an estimated 15% of new cancer cases in men (second only to prostate cancer) and 13% of new cancer cases in women (second only to breast cancer) [1]. Some evidence suggests that early detection of lung cancer may allow for more timely therapeutic intervention and hence a more favorable prognosis for the patient [2, 3].
An estimated 30-40% of potentially detectable lung cancers are missed by radiologists using conventional chest radiographs [4]. It is widely recognized, however, that the sensitivity of computerized tomography (CT) scans for lung nodule detection is superior to that of chest radiography [5-7]. For this reason, the CT scan is generally regarded as the “gold standard” for confirming the presence of a nodule. The most important advantage of CT is its ability to distinctly represent anatomic structures that would otherwise project in superposition in a chest radiograph; CT scans acquire three-dimensional volumetric data, while the image captured by a radiograph collapses real-world objects (i.e., patient anatomy) into two dimensions. The practical consequence of this difference in imaging approaches is that the average size of peripheral cancers missed by radiologists on CT scans was found to be 0.3 cm compared with 1.3 cm at radiography [8].
Although the potential camouflaging effect of overlapping anatomic structures is effectively eliminated in CT scans, identification of small lung nodules is still confounded by the prominence of blood vessels in CT images. Croisille et al. [9] demonstrated a significant improvement in radiologists' detection of nodules when vessels were removed from the images through region growing in three dimensions. Distinguishing between nodules and vessels typically requires visual comparison among multiple CT sections, each of which contains information that must be evaluated by a radiologist and assimilated into the larger context of the volumetric data acquired during the scan. This process could lead to fatigue or distraction, especially when other abnormalities are present [10]. The evaluation of CT scans for lung nodules requires the radiologist to mentally construct a three-dimensional representation of patient anatomy based on over 50 images acquired during the examination. This task, while cumbersome for radiologists, may be efficiently handled by a computerized method.
Despite the number of images that must be interpreted to detect nodules in CT scans, few investigators have participated in the development of computer-aided diagnostic (CAD) techniques for this task [11-15]. Ryan et al. [12] modeled nodules and vessels as spherical and cylindrical volumes, respectively. A comparison between soft tissue and air densities on the surface and within the volume of a bounding cube was used to differentiate nodules and vessels. Their method attained a sensitivity of 100% on an unspecified number of cases with an unreported number of false positives per case.
Kanazawa et al. [13] utilized a fuzzy clustering algorithm to identify vessels and potential nodules. A rule-based approach that incorporated distance from the lung boundary and circularity information was used to distinguish nodules from vessels in each section. The reported results appear to indicate that their algorithm attained a sensitivity of 86% with 11 false positive cases for a database of 224 cases.
Okumura et al. [14] used spatial filtering to automatically detect nodules. In a database of 82 cases, all 21 nodules were detected along with 301 false-positive regions.
Giger et al. [11] developed an automated detection scheme based on a database of eight CT scans. To distinguish nodules from vessels within the lung regions, geometric feature analysis was implemented in conjunction with multiple gray-level thresholding. Final classification was made based on a comparison of suspected regions in each section with suspected regions in adjacent sections. The method performed at a level of 94% sensitivity with an average of 1.25 false-positive detections per case.
More recently, Armato et al. [15, 16] applied the above method to a database of 17 helical CT scans and extended the method to include an artificial neural network (ANN) [17] to distinguish between nodules and vessels. Receiver operating characteristic (ROC) analysis [18] was used to evaluate the ability of the ANN to distinguish between nodules and non-nodules among the candidates selected within the segmented lung regions. The area under the ROC curves attained average values between 0.90 and 0.99.
A three-dimensional approach was reported by Armato et al. [19, 20] for a 17-case database. Linear discriminant analysis to classify identified nodule candidates as nodule or non-nodule yielded an overall nodule detection performance of 70% sensitivity with an average of three false-positive detections per section. Three-dimensional features were found to substantially contribute to the discrimination ability of the linear discriminant analysis classifier.
Fiebich et al. [21] recently developed a computerized method for the detection of lung nodules in low-dose helical CT scans from a lung cancer screening program. The method attained an overall nodule detection sensitivity of 95.6% with approximately 15 false-positive detections per study. Other investigators have contributed recently to the important task of computerized lung nodule detection in CT images [22-24].
Many institutions use a helical CT protocol in which image data is continuously acquired as the patient table is translated through the scanner [25]. Helical CT offers several advantages over the “step-and-shoot” acquisition of conventional CT, including decreased scanning time, improved patient throughput, reduced motion artifacts, the ability to perform a single-breath-hold scan, and the ability to retrospectively select the planes of image reconstruction [26]. These last two advantages are of particular importance to lung nodule detection. A scan performed during a single breath hold eliminates misregistration that may occur between sections due to respiration differences, and the ability to retrospectively select reconstruction planes allows for images that optimally capture a region of interest. Consequently, the major causes of false-negative conventional CT examinations may be eliminated with helical CT [27]. The sensitivity of helical CT for detection of nodules by radiologists is significantly superior to that of conventional CT, leading investigators to rate helical CT as the preferred radiologic modality for the detection of lung nodules [27].
The advantages of helical CT could result in its implementation as a modality for lung cancer screening [28]. Moreover, scans may be acquired with lower x-ray exposure to reduce the dose to screened individuals [29, 30]. Trials to validate lung cancer screening with low-dose helical CT are currently underway in the United States [31], Germany, and Japan [14].
However, due shortcomings in the above-noted methods, an improved method for the detection of lung nodules in thoracic CT scans is desirable.